The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Fu, W. AU - Brown, K.A. AU - D'Ottavio, T. AU - Dyer, P.S. AU - Nemesure, S. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - Accelerator Control Data Mining with WEKA J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - Accelerator control systems generates and stores many time-series data related to the performance of an accelerator and its support systems. Many of these time series data have detectable change trends and patterns. Being able to timely detect and recognize these data change trends and patterns, analyse and predict the future data changes can provide intelligent ways to improve the controls system with proactive feedback/forward actions. With the help of advanced data mining and machine learning technology, these types of analyses become easier to produce. As machine learning technology matures with the inclusion of powerful model algorithms, data processing tools, and visualization libraries in different programming languages (e.g. Python, R, Java, etc), it becomes relatively easy for developers to learn and apply machine learning technology to online accelerator control system data. This paper explores time series data analysis and forecasting in the Relativistic Heavy Ion Collider (RHIC) control systems with the Waikato Environment for Knowledge Analysis (WEKA) system and its Java data mining APIs. PB - JACoW Publishing CP - Geneva, Switzerland SP - 293 EP - 296 KW - controls KW - target KW - database KW - network KW - GUI DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-MOPHA043 UR - https://jacow.org/icalepcs2019/papers/mopha043.pdf ER -